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      Geohash
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        <h2 id="u7B80_u4ECB"><a href="#u7B80_u4ECB" class="headerlink" title="简介"></a>简介</h2><p>　　在gis领域中，空间运算操作，比如周边搜索等，服务端实现基本上采用借助于oracle spatial等数据库提供的空间运算能力，直接进行空间计算，得到结果并返回，这种模式在应对互联网高并发的情况下存在一定的性能问题。<br>　　在互联网应用场景下，在很多应用中，并不需要gis专业领域中非常精确地计算结果。同时考虑到国防安全，也不可能直接在互联网上提供高精度的空间数据。在这种情况下，Geohash就横空出世了！</p>
<h2 id="u7279_u70B9"><a href="#u7279_u70B9" class="headerlink" title="特点"></a>特点</h2><p>　　1.  GeoHash将二维的经纬度转换成字符串，比如下图展示了北京9个区域的GeoHash字符串，分别是WX4ER，WX4G2、WX4G3等等，每一个字符串代表了某一矩形区域。也就是说，这个矩形区域内所有的点（经纬度坐标）都共享相同的GeoHash字符串，这样可以保护隐私的同时（只表示大概区域位置而不是具体的点），又比较容易做缓存。比如左上角这个区域内的**WX4ER区域的用户不断发送位置信息请求附近餐馆数据，所以可以把key=WX4ER，value=该区域的餐馆信息进行缓存.而如果不使用GeoHash的话，由于区域内的用户传来的经纬度是各不相同的，很难做缓存。<img src="/images/geohash1.jpg" alt="geohash1.jpg"><br>　　2. 字符串越长，表示的范围越精确。如图所示，5位的编码能表示10平方千米范围的矩形区域，而6位编码能表示更精细的区域（约0.34平方千米）<img src="/images/geohash2.jpg" alt="geohash2.jpg"><br>　　3. 字符串相似的表示距离相近（特殊情况后文阐述），这样可以利用字符串的前缀匹配来查询附近的POI信息。如下两个图所示，一个在城区，一个在郊区，城区的GeoHash字符串之间比较相似，郊区的字符串之间也比较相似，而城区和郊区的GeoHash字符串相似程度要低些。<img src="/images/geohash3.jpg" alt="geohash3.jpg"><br>　　到这里我们可以知道GeoHash就是一种将经纬度转换成字符串的方法，并且使得在大部分情况下，字符串前缀匹配越多的距离越近。根据所在位置查询来查询附近餐馆时，只需要将所在位置经纬度转换成GeoHash字符串，并与各个餐馆的GeoHash字符串进行前缀匹配，匹配越多的距离越近。</p>
<h2 id="u5B9E_u73B0"><a href="#u5B9E_u73B0" class="headerlink" title="实现"></a>实现</h2><p>　　下面以北海公园为例介绍GeoHash算法的计算步骤：<br>　　1. 根据经纬度计算GeoHash二进制编码：地球纬度区间是[-90,90]， 北海公园的纬度是39.928167，可以通过下面算法对纬度39.928167进行逼近编码:<br>　　1）区间[-90,90]进行二分为[-90,0),[0,90]，称为左右区间，可以确定39.928167属于右区间[0,90]，给标记为1；<br>　　2）接着将区间[0,90]进行二分为 [0,45),[45,90]，可以确定39.928167属于左区间 [0,45)，给标记为0；<br>　　3）递归上述过程39.928167总是属于某个区间[a,b]。随着每次迭代区间[a,b]总在缩小，并越来越逼近39.928167；<br>　　4）如果给定的纬度x（39.928167）属于左区间，则记录0，如果属于右区间则记录1，这样随着算法的进行会产生一个序列1011100，序列的长度跟给定的区间划分次数有关。<br>　　根据纬度算编码：<img src="/images/geohash-lat.jpg" alt="geohash-lat.jpg"><br>　　同理，地球经度区间是[-180,180]，可以对经度116.389550进行编码：<img src="/images/geohash-lon.jpg" alt="geohash-lon.jpg"><br>　　2. 组码：通过上述计算，纬度产生的编码为10111 00011，经度产生的编码为11010 01011。偶数位放经度，奇数位放纬度，把2串编码组合生成新串：11100 11101 00100 01111。最后使用0-9、b-z（去掉a, i, l, o）这32个字母进行base32编码，首先将11100 11101 00100 01111转成十进制(每五位为一个单位，最大取值为11111=31)，对应着28、29、4、15，十进制对应的base32编码就是wx4g。同理，将编码转换成经纬度的解码算法与之相反，具体不再赘述。<img src="/images/geohash-base32.jpg" alt="geohash-base32.jpg"></p>
<h2 id="u539F_u7406"><a href="#u539F_u7406" class="headerlink" title="原理"></a>原理</h2><p>　　上文讲了GeoHash的计算步骤，仅仅说明是什么而没有说明为什么？为什么分别给经度和维度编码？为什么需要将经纬度两串编码交叉组合成一串编码？本节试图回答这一问题。<br>　　如图所示，我们将二进制编码的结果填写到空间中，当将空间划分为四块时候，编码的顺序分别是左下角00，左上角01，右下脚10，右上角11，也就是类似于Z的曲线，当我们递归的将各个块分解成更小的子块时，编码的顺序是自相似的（分形），每一个子快也形成Z曲线，这种类型的曲线被称为Peano空间填充曲线。<br>　　这种类型的空间填充曲线的优点是将二维空间转换成一维曲线（事实上是分形维），对大部分而言，编码相似的距离也相近， 但Peano空间填充曲线最大的缺点就是突变性，有些编码相邻但距离却相差很远，比如0111与1000，编码是相邻的，但距离相差很大。<img src="/images/geohash-peano.jpg" alt="geohash-peano.jpg"><br>　　除Peano空间填充曲线外，还有很多空间填充曲线，如图所示，其中效果公认较好是Hilbert空间填充曲线，相较于Peano曲线而言，Hilbert曲线没有较大的突变。为什么GeoHash不选择Hilbert空间填充曲线呢？可能是Peano曲线思路以及计算上比较简单吧，事实上，Peano曲线就是一种四叉树线性编码方式。<img src="/images/geohash-arth.jpg" alt="geohash-arth.jpg"></p>
<h2 id="u7F3A_u70B9"><a href="#u7F3A_u70B9" class="headerlink" title="缺点"></a>缺点</h2><p>　　如果两个点靠的很近，但是被划分到了两个区域，会导致两个点的编码差别比距离更远的点更大，如下图所示：<img src="/images/geohash-bug.jpg" alt="geohash-bug.jpg"><br>　　红色的点是我们的位置，绿色的两个点分别是附近的两个餐馆，但是在查询的时候会发现距离较远餐馆的GeoHash编码与我们一样（因为在同一个GeoHash区域块上），而较近餐馆的GeoHash编码与我们不一致。解决的思路很简单，<strong>除了使用定位点的GeoHash编码进行匹配外，还使用周围8个区域的GeoHash编码</strong>，这样可以避免这个问题。<br>　　已经知道现有的GeoHash算法使用的是Peano空间填充曲线，这种曲线会产生突变，造成了编码虽然相似但距离可能相差很大的问题，因此在查询附近餐馆时候，首先筛选GeoHash编码相似的POI点，然后进行实际距离计算。</p>
<h2 id="u53C2_u8003_u8FDE_u63A5"><a href="#u53C2_u8003_u8FDE_u63A5" class="headerlink" title="参考连接"></a>参考连接</h2><p><a href="https://en.wikipedia.org/wiki/Geohash" target="_blank" rel="external">https://en.wikipedia.org/wiki/Geohash</a><br><a href="http://blog.jobbole.com/80633/" target="_blank" rel="external">http://blog.jobbole.com/80633/</a></p>

      
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